Understanding animal movement and behaviour can aid spatial planning and inform conservation management. However, it is difficult to directly observe behaviours in remote and hostile terrain such as the marine environment. Behaviours can be inferred from telemetry data using hidden Markov models (HMMs), but model predictions are not typically validated due to difficulty obtaining ‘ground truth’ behavioural information. We investigate the accuracy of HMM-inferred behaviours by considering a unique dataset provided by Joint Nature Conservation Committee. The data consist of simultaneous proxy movement tracks of the boat (defined as visual tracks as birds are followed by eye) and seabird behaviour obtained at the same time-frequency by observers on the boat. We use these data to assess whether (i) visual track is a good proxy for true bird locations in relation to HMM-inferred behaviours, and (ii) inferred behaviours from HMMs fitted to visual tracking data accurately represent true behaviours as identified by behavioural observations taken from the boat. We demonstrate that visual tracking data can be regarded as a good proxy for true movement data of birds in terms of similarity in inferred behaviours. Accuracy of HMMs ranging from 71% to 87% during chick-rearing and 54% to 70% during incubation was generally insensitive to model choice, even when AIC values varied substantially across different models. Finally, we show that for foraging, a state of primary interest for conservation purposes, identified missed foraging bouts lasted for only a few seconds. We conclude that HMMs fitted to tracking data can accurately identify important conservation-relevant behaviours, demonstrated using visual tracking data. Therefore, confidence in using HMMs for behavioural inference should increase even when validation data are unavailable. This has important implications for animal conservation, where the size and location of protected areas are often informed by behaviours identified using HMMs fitted to movement data.

Population dynamics are functions of several demographic processes including survival, reproduction, somatic growth, and maturation. The rates or probabilities for these processes can vary by time, by location, and by individual. These processes can co-vary and interact to varying degrees, e.g., an animal can only reproduce when it is in a particular maturation state. Population dynamics models that treat the processes as independent may yield somewhat biased or imprecise parameter estimates, as well as predictions of population abundances or densities. However, commonly used integral projection models (IPMs) typically assume independence across these demographic processes. We examine several approaches for modelling between process dependence in IPMs, and include cases where the processes co-vary as a function of time (temporal variation), co-vary within each individual (individual heterogeneity), and combinations of these (temporal variation and individual heterogeneity). We compare our methods to conventional IPMs, which treat vital rates independent, using simulations and a case study of Soay sheep (Ovis aries). In particular, our results indicate that correlation between vital rates can moderately affect variability of some population-level statistics. Therefore, including such dependent structures is generally advisable when fitting IPMs to ascertain whether or not such between vital rate dependencies exist, which in turn can have subsequent impact on population management or life-history evolution.